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I got a 80% return with a simple modification of the sample algorithm.

Does it make sense?

Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 51f9f396d13ebb06bac5ca15
This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
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4 responses

@Zhiyong, it's possible to fit any curve by tweaking backtest numbers. This result is not generalizable to other stocks or time periods.

Right - you may have over fitted your data or done some "data mining".

Here's how I explain this to people: There are a million different ways to write an algorithm that goes long on Apple in 2008. If you did that, you made money - Apple went up. You need to write an algorithm that is smart enough to pick Apple out of the sea of choices in 2008, OR you need to write an algorithm that can make money even when the underlying stock isn't a rocketship.

Backtesting is great because it lets your rule out the obvious losers. But when you get an algorithm that looks good, that's when it gets even harder. You have to scrutinize it again and make sure you're not fooling yourself. There are a couple good ways to do that. One is to hold back some data, called "out of sample data" and then at the very end, test the out-of-sample data. The other is paper trading. You can never overfit papertrading.


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^^Well said Dan. I also notice that algorithms that switch to cash or commodities just before the 2008 crash seem to beat benchmark by upwards of 100% but might have been trailing before that.